I have always liked to think that imitation is the sincerest form of flattery, but today I am finding that feeling tinged with a bit of anger as well. I was playing around with GitHub's API and came across a project that borrows quite a bit of code and ideas from Peewee ORM.
At first I was excited to see another Python coder writing a lightweight ORM. I figured it would be neat to see how someone else would approach the problem. To my surprise, though, I found that most of the ideas, class hierarchies, and APIs were lifted directly from Peewee!
Redis is one of the more unique NoSQL offerings to have become popular over the past five years. It seems that there is no limit to the use-cases one can find for Redis. It's fantastic as a cache, doubles as a task-queue, can provide fast type-ahead search, and much more. The idea that you can store data-structures instead of rows and columns, keys and values, or JSON documents strikes me as particularly innovative. A while back I released walrus, a collection of Python utilities I'd built to simplify some of these use-cases and provide Pythonic APIs for the data-structures Redis natively supports. If you're a Python developer you might check it out.
Recently I've learned about a few new Redis-like databases: Rlite, Vedis and LedisDB. Each of these projects offers a slightly different take on the data-structure server you find in Redis, so I thought that I'd take some time and see how they worked. In this post I'll share what I've learned, and also show you how to use these databases with Walrus, as I've added support for them in the latest 0.3.0 release.
In the Limitations section of the
README, Salvatore has written:
Disque was designed a bit in astronaut mode, not triggered by an actual use case of mine, but more in response to what I was seeing people doing with Redis as a message queue and with other message queues.
This admission makes me wary of using Disque, even if it reaches a stable release, because of my own experience with similar projects I've created but never actually used. These projects are usually fun opportunities for learning, but when it comes to maintenance, my experience has shown me that they quickly become a burden. Usually the problem is masked by the fact that if I'm not using it usually nobody else is either, but in the rare case I do end up with users, then eventually those users are going to submit bug reports and feature requests.
For a problem as complex as a distribute message broker, I imagine that there are going to be a lot of bug reports, strange edge-cases, and feature requests to support exotic use-cases. I hope that, in addition to his work on Redis, Salvatore can find the time to support Disque!
The other reason I don't foresee using Disque is alluded to in the author's own comments. He observes that many people are using Redis as a message broker, and decides that maybe there is a need for a "Redis of messaging". I would say the opposite is true, and that instead of another message server, people want to use Redis!
Redis integrates very nicely into the stack for web-based projects. It can be used as a cache, for locking, as a primary data store, for write-heavy portions of the application, and yes, as a message broker.
Perhaps the reason people are using Redis as a message broker is because they don't want to use something else?
In this post I'll describe how to implement tagging with a relational database. What I mean by tagging are those little labels you see at the top of this blog post, which indicate how I've chosen to categorize the content. There are many ways to solve this problem, and I'll try to describe some of the more popular methods, as well as one unconventional approach using bitmaps. In each section I'll describe the database schema, try to list the benefits and drawbacks, and present example queries. I will use Peewee ORM for the example code, but hopefully these examples will easily translate to your tool-of-choice.
In my continuing adventures with SQLite, I had the idea of writing a RESTful search server utilizing SQLite's full-text search extension. You might think of it as a poor man's ElasticSearch – a very, very poor man.
So what is this project? Well, the idea I had was that instead of building out separate search implementations for my various projects, I would build a single lightweight search service I could use everywhere. I really like SQLite (and have previously blogged about using SQLite's full-text search with Python), and the full-text search extension is quite good, so it didn't require much imagination to take the next leap and expose it as a web-service.
Read on for more details.